{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/UDOP/Fine_tune_UDOPEncoderModel_on_FUNSD_(HuggingFace_Trainer).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mfj8f-IeulH1"
      },
      "source": [
        "# Fine-tune UDOP, encoder-only, on the FUNSD dataset\n",
        "\n",
        "[UDOP](https://huggingface.co/docs/transformers/main/en/model_doc/udop) is an exciting document AI model by Microsoft Research. It consists of a vision encoder and a text decoder, similar to models like [Donut](https://huggingface.co/docs/transformers/en/model_doc/donut) and [Pix2Struct](https://huggingface.co/docs/transformers/model_doc/pix2struct). It's a generative model, where we first encode the document image into embeddings, which are then used to condition a text-decoder (GPT-like) to generate the text we want, like structured JSON.\n",
        "\n",
        "<img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/udop_architecture.jpg\"\n",
        "alt=\"drawing\" width=\"600\"/>\n",
        "\n",
        "<small> UDOP architecture. Taken from the <a href=\"https://arxiv.org/abs/2212.02623\">original paper.</a> </small>\n",
        "\n",
        "However, one could also simply use the encoder-only part of UDOP, add a linear classifier on top, and fine-tune it altogether on a labeled dataset, very much like how one would train a BERT, LayoutLMv2 or LayoutLMv3 model. That's what we're going to do in this notebook! This is useful in case we don't require the generative part of UDOP.\n",
        "\n",
        "## Set-up environment\n",
        "\n",
        "First, we install 🤗 Transformers, as well as 🤗 Datasets and Seqeval (the latter is useful for evaluation metrics such as F1 on sequence labeling tasks)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSJSQLi7unbI",
        "outputId": "38877355-e070-43bc-ddcf-89ac3b7c14a6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'transformers': No such file or directory\n",
            "Cloning into 'transformers'...\n",
            "remote: Enumerating objects: 191396, done.\u001b[K\n",
            "remote: Counting objects: 100% (977/977), done.\u001b[K\n",
            "remote: Compressing objects: 100% (552/552), done.\u001b[K\n",
            "remote: Total 191396 (delta 519), reused 726 (delta 370), pack-reused 190419\u001b[K\n",
            "Receiving objects: 100% (191396/191396), 166.91 MiB | 25.34 MiB/s, done.\n",
            "Resolving deltas: 100% (141488/141488), done.\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m883.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m84.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEhAXR6ZrPPR",
        "outputId": "ceb46023-ef4c-458c-af96-77b9ae7276db"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m485.6/485.6 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q datasets seqeval sentencepiece accelerate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LIJRzTbqrclo"
      },
      "source": [
        "## Load dataset\n",
        "\n",
        "Next, we load a dataset from the 🤗 [hub](https://huggingface.co/datasets/nielsr/funsd-layoutlmv3). This one is the [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset, a collection of annotated forms."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169,
          "referenced_widgets": [
            "02bc27bbcfe54162b57f137f8afa3b4c",
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            "73fd447a73c44f0e939c40952402b78e",
            "4894125de6ca42dbbcfb3ed2ed36c6bf",
            "024fd23d8a4740a59fb36dfd5b20266c",
            "5c1bf69e12a349f997d63a3fcfc18007",
            "4884f65a9dd941fc9ec8f9b3ba138f46",
            "795338e9cab94c519d7a1e557243ec38",
            "167765337be84a1b8a1e2453606f8609",
            "dbce2f7f51de420d8e7b86dd25e428b0",
            "e07efe1eb937439480ec4775ba79b5bf",
            "27fe6b1956e940a1a56966155eaa0cb9",
            "6e8a6226d6ee41f9be4d3ad15928d336",
            "a71324d8ad5b46eca05e0ebd808bfd21",
            "94b7232ac129471ab1a99772c60d40bb",
            "d498f22777d6401d88e7a80a16163773",
            "6fbcca63aad14f578cccc895d58a7dc7",
            "85061e16d5c841ef9c21c1f66816e9e3",
            "c212b6396aa74c70b7ae6896e2e27e23",
            "3640bfa409d142e395b1345ae4dae08e",
            "f7f273b76fbc470e858455a95c00485b",
            "3bb519ea170f42ecb70fbeb69fff3588",
            "c2538b07c29b4d06a69ceebf235714b6",
            "02838bdba2ad4594aee7810e6bfe241e",
            "592bb8728cb84fee90752b36a988bf23",
            "70d51873421d4bc9a9b435d5affc8383",
            "77cb3f69befc4ae889dbe7cedeeda68b",
            "a9e2519f305a4532bd133bf8fd4f5938",
            "c3aad959f0f547059aa668198de69391",
            "26a647117aed46aebd393c462f907152",
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            "df71c3781ef0496ba79dfa17a999f3a3",
            "c4a2e910e0c1446ea9e54093d617078c",
            "39d1f20af2164f2d8289282914bae738",
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            "d982185727ec435280dbbbcd1a967a17",
            "a916d55d84f9432997915c941710cc94",
            "48ad2e2b3c8d47b6a6ebe1aaef6611be",
            "ec1eeb5a5ddd42078fa282bfbbfc2450",
            "2047bb76f3de43289b1e1475657de268",
            "da9f85c250554b4391ca393d05b9a99c",
            "62ffafdcfe134cec95e3703686938cd4",
            "c39131e167cb46c981a67a679a2f5618",
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            "fcd2030476a248a8bba238aa14479ece",
            "0cd48f13a8584e9fbd17bf0530be1114"
          ]
        },
        "id": "P-BqQFmArQ-o",
        "outputId": "0e1e0935-724c-4e77-be77-8c425c6e8f66"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "02bc27bbcfe54162b57f137f8afa3b4c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.13k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset funsd-layoutlmv3/funsd to /root/.cache/huggingface/datasets/nielsr___funsd-layoutlmv3/funsd/1.0.0/0e3f4efdfd59aa1c3b4952c517894f7b1fc4d75c12ef01bcc8626a69e41c1bb9...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e07efe1eb937439480ec4775ba79b5bf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/16.8M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3bb519ea170f42ecb70fbeb69fff3588",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c4a2e910e0c1446ea9e54093d617078c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset funsd-layoutlmv3 downloaded and prepared to /root/.cache/huggingface/datasets/nielsr___funsd-layoutlmv3/funsd/1.0.0/0e3f4efdfd59aa1c3b4952c517894f7b1fc4d75c12ef01bcc8626a69e41c1bb9. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d982185727ec435280dbbbcd1a967a17",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# this dataset uses the new Image feature :)\n",
        "dataset = load_dataset(\"nielsr/funsd-layoutlmv3\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2BP1WMNPj6A"
      },
      "source": [
        "As we can see, the dataset consists of 2 splits (\"train\" and \"test\"), and each example contains a list of words (\"tokens\") with corresponding boxes (\"bboxes\"), and the words are tagged (\"ner_tags\"). Each example also include the original image (\"image\")."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BI7hZo4Fzcrc",
        "outputId": "42547993-5486-4920-bfcb-43f433388f4f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'tokens', 'bboxes', 'ner_tags', 'image'],\n",
              "        num_rows: 149\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'tokens', 'bboxes', 'ner_tags', 'image'],\n",
              "        num_rows: 50\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l5R0xubIQW1P"
      },
      "source": [
        "Let's check the features:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "REUV3z-o0Hc1",
        "outputId": "72b0546e-8cb2-459b-c549-9a8fc8ff6348"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'id': Value(dtype='string', id=None),\n",
              " 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n",
              " 'bboxes': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None),\n",
              " 'ner_tags': Sequence(feature=ClassLabel(names=['O', 'B-HEADER', 'I-HEADER', 'B-QUESTION', 'I-QUESTION', 'B-ANSWER', 'I-ANSWER'], id=None), length=-1, id=None),\n",
              " 'image': Image(decode=True, id=None)}"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset[\"train\"].features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aagYK5p7P3yN"
      },
      "source": [
        "Note that you can directly see the example in a notebook (as the \"image\" column is of type [Image](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/main_classes#datasets.Image))."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jEHwig35P7jJ",
        "outputId": "969d7c74-14bc-4e36-ebce-328bd219f13a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=762x1000 at 0x7F5D1BB2A950>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example = dataset[\"train\"][0]\n",
        "example[\"image\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXNUH4o4Qf7M",
        "outputId": "cffeac4e-29e6-450d-cea8-0fa4b3b01217"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['R&D', ':', 'Suggestion:', 'Date:', 'Licensee', 'Yes', 'No', '597005708', 'R&D', 'QUALITY', 'IMPROVEMENT', 'SUGGESTION/', 'SOLUTION', 'FORM', 'Name', '/', 'Phone', 'Ext.', ':', 'M.', 'Hamann', 'P.', 'Harper,', 'P.', 'Martinez', '9/', '3/', '92', 'R&D', 'Group:', 'J.', 'S.', 'Wigand', 'Supervisor', '/', 'Manager', 'Discontinue', 'coal', 'retention', 'analyses', 'on', 'licensee', 'submitted', 'product', 'samples', '(Note', ':', 'Coal', 'Retention', 'testing', 'is', 'not', 'performed', 'by', 'most', 'licensees.', 'Other', 'B&W', 'physical', 'measurements', 'as', 'ends', 'stability', 'and', 'inspection', 'for', 'soft', 'spots', 'in', 'ciparettes', 'are', 'thought', 'to', 'be', 'sufficient', 'measures', 'to', 'assure', 'cigarette', 'physical', 'integrity.', 'The', 'proposed', 'action', 'will', 'increase', 'laboratory', 'productivity', '.', ')', 'Suggested', 'Solutions', '(s)', ':', 'Delete', 'coal', 'retention', 'from', 'the', 'list', 'of', 'standard', 'analyses', 'performed', 'on', 'licensee', 'submitted', 'product', 'samples.', 'Special', 'requests', 'for', 'coal', 'retention', 'testing', 'could', 'still', 'be', 'submitted', 'on', 'an', 'exception', 'basis.', 'Have', 'you', 'contacted', 'your', 'Manager/', 'Supervisor?', 'Manager', 'Comments:', 'Manager,', 'please', 'contact', 'suggester', 'and', 'forward', 'comments', 'to', 'the', 'Quality', 'Council.', 'qip', '.', 'wp']\n",
            "[[383, 91, 493, 175], [287, 316, 295, 327], [124, 355, 221, 370], [632, 268, 679, 282], [670, 309, 748, 323], [604, 605, 633, 619], [715, 603, 738, 617], [688, 904, 841, 926], [335, 201, 555, 229], [335, 201, 555, 229], [335, 201, 555, 229], [335, 201, 555, 229], [335, 201, 555, 229], [335, 201, 555, 229], [116, 272, 267, 289], [116, 272, 267, 289], [116, 272, 267, 289], [116, 272, 267, 289], [116, 272, 267, 289], [282, 271, 591, 287], [282, 271, 591, 287], [282, 271, 591, 287], [282, 271, 591, 287], [282, 271, 591, 287], [282, 271, 591, 287], [712, 264, 774, 279], [712, 264, 774, 279], [712, 264, 774, 279], [551, 310, 644, 323], [551, 310, 644, 323], [309, 313, 429, 327], [309, 313, 429, 327], [309, 313, 429, 327], [119, 316, 286, 331], [119, 316, 286, 331], [119, 316, 286, 331], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [249, 346, 779, 447], [124, 486, 328, 504], [124, 486, 328, 504], [124, 486, 328, 504], [124, 486, 328, 504], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [345, 483, 778, 553], [125, 608, 522, 624], [125, 608, 522, 624], [125, 608, 522, 624], [125, 608, 522, 624], [125, 608, 522, 624], [125, 608, 522, 624], [128, 651, 276, 665], [128, 651, 276, 665], [304, 644, 717, 662], [304, 644, 717, 662], [304, 644, 717, 662], [304, 644, 717, 662], [304, 644, 717, 662], [304, 644, 717, 662], [129, 662, 423, 677], [129, 662, 423, 677], [129, 662, 423, 677], [129, 662, 423, 677], [129, 662, 423, 677], [133, 823, 190, 838], [133, 823, 190, 838], [133, 823, 190, 838]]\n",
            "[0, 3, 3, 3, 5, 3, 3, 0, 1, 2, 2, 2, 2, 2, 3, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 5, 6, 6, 3, 4, 5, 6, 6, 3, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 3, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 1, 2, 2, 2, 2, 2, 3, 4, 5, 6, 6, 6, 6, 6, 5, 6, 6, 6, 6, 0, 0, 0]\n"
          ]
        }
      ],
      "source": [
        "words, boxes, ner_tags = example[\"tokens\"], example[\"bboxes\"], example[\"ner_tags\"]\n",
        "print(words)\n",
        "print(boxes)\n",
        "print(ner_tags)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eHiLE4PYtSpC"
      },
      "source": [
        "## Prepare dataset\n",
        "\n",
        "Next, we prepare the dataset for the model. This can be done very easily using `UdopProcessor`, which internally wraps a `UdopImageProcessor` (for the image modality) and a `UdopTokenizer` (for the text modality) into one.\n",
        "\n",
        "Basically, the processor does the following internally:\n",
        "* the feature extractor is used to resize + normalize each document image into `pixel_values`\n",
        "* the tokenizer is used to turn the words, boxes and NER tags into token-level `input_ids`, `attention_mask` and `labels`.\n",
        "\n",
        "The processor simply returns a dictionary that contains all these keys."
      ]
    },
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        },
        "id": "Yq5FuP6SyJD4",
        "outputId": "aae0408b-6d7f-4453-8fc1-2e670ed278a4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f263ce78738a4ae2af8252e881417166",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)rocessor_config.json:   0%|          | 0.00/443 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d6e926dee51046de9eb0570ebf0a151c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/24.1k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fc9980ac22664faea407f18162c2de27",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4c67fa3863264114add8891585f5ac29",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.63M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b70b36fb6bd54e0c9125334ba78de46e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/27.0k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fdf2659d7fc94cfdb96f921a69b6d501",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/23.7k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoProcessor\n",
        "\n",
        "# we'll use the Auto API here - it will load UdopProcessor behind the scenes,\n",
        "# based on the checkpoint we provide from the hub\n",
        "# we specify apply_ocr=False here as we already have used an OCR engine\n",
        "processor = AutoProcessor.from_pretrained(\"microsoft/udop-large\", apply_ocr=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-yymv3asRdVA"
      },
      "source": [
        "We'll first create `id2label` and label2id mappings, useful for inference. Note that `UdopForTokenClassification` (the model we'll use later on) will simply output an integer index for a particular class (for each token), so we still need to map it to an actual class name."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VmQCS1Ptz_OP"
      },
      "outputs": [],
      "source": [
        "from datasets.features import ClassLabel\n",
        "\n",
        "features = dataset[\"train\"].features\n",
        "column_names = dataset[\"train\"].column_names\n",
        "image_column_name = \"image\"\n",
        "text_column_name = \"tokens\"\n",
        "boxes_column_name = \"bboxes\"\n",
        "label_column_name = \"ner_tags\"\n",
        "\n",
        "# In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the\n",
        "# unique labels.\n",
        "def get_label_list(labels):\n",
        "    unique_labels = set()\n",
        "    for label in labels:\n",
        "        unique_labels = unique_labels | set(label)\n",
        "    label_list = list(unique_labels)\n",
        "    label_list.sort()\n",
        "    return label_list\n",
        "\n",
        "if isinstance(features[label_column_name].feature, ClassLabel):\n",
        "    label_list = features[label_column_name].feature.names\n",
        "    # No need to convert the labels since they are already ints.\n",
        "    id2label = {k: v for k,v in enumerate(label_list)}\n",
        "    label2id = {v: k for k,v in enumerate(label_list)}\n",
        "else:\n",
        "    label_list = get_label_list(dataset[\"train\"][label_column_name])\n",
        "    id2label = {k: v for k,v in enumerate(label_list)}\n",
        "    label2id = {v: k for k,v in enumerate(label_list)}\n",
        "num_labels = len(label_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MFX9kl-I0PMk",
        "outputId": "737e1988-081b-44a5-ef6f-7914b42d8d73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['O', 'B-HEADER', 'I-HEADER', 'B-QUESTION', 'I-QUESTION', 'B-ANSWER', 'I-ANSWER']\n"
          ]
        }
      ],
      "source": [
        "print(label_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQVhLKTwQkVp",
        "outputId": "0019cd19-8cf0-4faa-b774-14121a15e1fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{0: 'O', 1: 'B-HEADER', 2: 'I-HEADER', 3: 'B-QUESTION', 4: 'I-QUESTION', 5: 'B-ANSWER', 6: 'I-ANSWER'}\n"
          ]
        }
      ],
      "source": [
        "print(id2label)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r73QM4b7R289"
      },
      "source": [
        "Next, we'll define a function which we can apply on the entire dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tiMrs3FFeIFK"
      },
      "outputs": [],
      "source": [
        "def prepare_examples(examples):\n",
        "  images = examples[image_column_name]\n",
        "  words = examples[text_column_name]\n",
        "  boxes = examples[boxes_column_name]\n",
        "  word_labels = examples[label_column_name]\n",
        "\n",
        "  encoding = processor(images, words, boxes=boxes, word_labels=word_labels,\n",
        "                       truncation=True, padding=\"max_length\")\n",
        "\n",
        "  return encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
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        },
        "id": "YHkC26CQelBH",
        "outputId": "b7b5517d-91a4-4b0d-91c1-f77e040f0543"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c3ecd3fbef52490bbe5a48ee762e67c8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/149 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1fb80610ad1f47ada1ee4f2a9ccf2e73",
              "version_major": 2,
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            "text/plain": [
              "Map:   0%|          | 0/50 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D\n",
        "\n",
        "# we need to define custom features for `set_format` (used later on) to work properly\n",
        "features = Features({\n",
        "    'pixel_values': Array3D(dtype=\"float32\", shape=(3, 224, 224)),\n",
        "    'input_ids': Sequence(feature=Value(dtype='int64')),\n",
        "    'attention_mask': Sequence(Value(dtype='int64')),\n",
        "    'bbox': Array2D(dtype=\"int64\", shape=(512, 4)),\n",
        "    'labels': Sequence(feature=Value(dtype='int64')),\n",
        "})\n",
        "\n",
        "train_dataset = dataset[\"train\"].map(\n",
        "    prepare_examples,\n",
        "    batched=True,\n",
        "    remove_columns=column_names,\n",
        "    features=features,\n",
        ")\n",
        "eval_dataset = dataset[\"test\"].map(\n",
        "    prepare_examples,\n",
        "    batched=True,\n",
        "    remove_columns=column_names,\n",
        "    features=features,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1PegGfOe06B",
        "outputId": "912becd5-dc2b-4785-f137-3048b6f06c9a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['pixel_values', 'input_ids', 'attention_mask', 'bbox', 'labels'],\n",
              "    num_rows: 149\n",
              "})"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "RvAN1tXafKWM",
        "outputId": "cde78837-d36e-4111-c2db-5ceb739496c6"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'R&D : Suggestion: Date: Licensee Yes No 597005708 R&D QUALITY IMPROVEMENT SUGGESTION/ SOLUTION FORM Name / Phone Ext. : M. Hamann P. Harper, P. Martinez 9/ 3/ 92 R&D Group: J. S. Wigand Supervisor / Manager Discontinue coal retention analyses on licensee submitted product samples (Note : Coal Retention testing is not performed by most licensees. Other B&W physical measurements as ends stability and inspection for soft spots in ciparettes are thought to be sufficient measures to assure cigarette physical integrity. The proposed action will increase laboratory productivity. ) Suggested Solutions (s) : Delete coal retention from the list of standard analyses performed on licensee submitted product samples. Special requests for coal retention testing could still be submitted on an exception basis. Have you contacted your Manager/ Supervisor? Manager Comments: Manager, please contact suggester and forward comments to the Quality Council. qip. wp</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>'"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example = train_dataset[0]\n",
        "processor.tokenizer.decode(example[\"input_ids\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xa13FTcnSE-B"
      },
      "source": [
        "Next, we set the format to PyTorch."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-YvpsU8vfp4j"
      },
      "outputs": [],
      "source": [
        "train_dataset.set_format(\"torch\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PQ0kcdrSI8P"
      },
      "source": [
        "Let's verify that everything was created properly:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHOf2cuJfw7B",
        "outputId": "853a45e2-b669-4615-da4e-54d54e6e5a1d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pixel_values torch.Size([3, 224, 224])\n",
            "input_ids torch.Size([512])\n",
            "attention_mask torch.Size([512])\n",
            "bbox torch.Size([512, 4])\n",
            "labels torch.Size([512])\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "example = train_dataset[0]\n",
        "for k,v in example.items():\n",
        "    print(k,v.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "xKXq17HZ1lf9",
        "outputId": "1e3456f8-b818-4cd3-e22f-c3cb273963eb"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'R&D : Suggestion: Date: Licensee Yes No 597005708 R&D QUALITY IMPROVEMENT SUGGESTION/ SOLUTION FORM Name / Phone Ext. : M. Hamann P. Harper, P. Martinez 9/ 3/ 92 R&D Group: J. S. Wigand Supervisor / Manager Discontinue coal retention analyses on licensee submitted product samples (Note : Coal Retention testing is not performed by most licensees. Other B&W physical measurements as ends stability and inspection for soft spots in ciparettes are thought to be sufficient measures to assure cigarette physical integrity. The proposed action will increase laboratory productivity. ) Suggested Solutions (s) : Delete coal retention from the list of standard analyses performed on licensee submitted product samples. Special requests for coal retention testing could still be submitted on an exception basis. Have you contacted your Manager/ Supervisor? Manager Comments: Manager, please contact suggester and forward comments to the Quality Council. qip. wp</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>'"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "processor.tokenizer.decode(train_dataset[0][\"input_ids\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5PIXhwETAlm",
        "outputId": "397a952c-3c6e-4f04-c044-8ee775edb949"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "for id, label in zip(train_dataset[0][\"input_ids\"], train_dataset[0][\"labels\"]):\n",
        "  if label.item() != -100:\n",
        "    print(processor.tokenizer.decode([id]), id2label[label.item()])\n",
        "  else:\n",
        "    print(processor.tokenizer.decode([id]), label.item())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbuC41BRUo6T"
      },
      "source": [
        "## Define metrics\n",
        "\n",
        "Next, we define a `compute_metrics` function, which is used by the Trainer to ... compute metrics.\n",
        "\n",
        "This function should take a named tuple as input, and return a dictionary as output as stated in the [docs](https://huggingface.co/docs/transformers/main_classes/trainer)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "2388c60b3cbe412a94fac68e42809520",
            "821ee063c0d94a6b86be568a8da64be9",
            "f964c64c6d0e43819ebc84a1bb7647b4",
            "1d7611408a95415caca1605567fb0575",
            "0774d90d523b4c24bbfb7c77c0a4f17f",
            "3018e658517f46e281fed8a80d8b4ebd",
            "942f26a44e5447b7b22714d2e7162c2d",
            "c346d0e442764fe4abea2ad5719a9a45",
            "269a947001b74454ab1c93beb7d6fc40",
            "b7cfe66350854895928d53bbce2a7c19",
            "0300821b8d574747b51296c84771677b"
          ]
        },
        "id": "vwP-I1s7Us0s",
        "outputId": "251d033d-eabd-455f-dd4b-d0b61ff1b814"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-20-edef154daec7>:3: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  metric = load_metric(\"seqeval\")\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2388c60b3cbe412a94fac68e42809520",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.47k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"seqeval\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VLiCLF9k3i4j"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "return_entity_level_metrics = False\n",
        "\n",
        "def compute_metrics(p):\n",
        "    predictions, labels = p\n",
        "    predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "    # Remove ignored index (special tokens)\n",
        "    true_predictions = [\n",
        "        [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "    true_labels = [\n",
        "        [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "\n",
        "    results = metric.compute(predictions=true_predictions, references=true_labels)\n",
        "    if return_entity_level_metrics:\n",
        "        # Unpack nested dictionaries\n",
        "        final_results = {}\n",
        "        for key, value in results.items():\n",
        "            if isinstance(value, dict):\n",
        "                for n, v in value.items():\n",
        "                    final_results[f\"{key}_{n}\"] = v\n",
        "            else:\n",
        "                final_results[key] = value\n",
        "        return final_results\n",
        "    else:\n",
        "        return {\n",
        "            \"precision\": results[\"overall_precision\"],\n",
        "            \"recall\": results[\"overall_recall\"],\n",
        "            \"f1\": results[\"overall_f1\"],\n",
        "            \"accuracy\": results[\"overall_accuracy\"],\n",
        "        }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DP2UUjvA4j-A"
      },
      "source": [
        "## Define the model\n",
        "\n",
        "Next we define the model: this is a Transformer encoder with pre-trained weights, and a randomly initialized head on top for token classification.\n",
        "\n",
        "Also note the warning we get when loading the weights: it is telling us that all the weights of UDOP's decoder aren't used, which is expected as we're only using the encoder part."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155,
          "referenced_widgets": [
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            "6836e5f2f4874c5695641c118d06a065",
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            "18d571ca4b3d42f7b552c59865981fd7",
            "3d21407ef7274c64bc6607cdb1d8bc3b",
            "14bd11928cb24d1495de1874e88f90b6",
            "848ca6603ebd4727970af3e20135675e"
          ]
        },
        "id": "BxUZi7R-395P",
        "outputId": "f8ede721-5bf4-44df-9578-f84e45d0d4ea"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "529956c1d32a435294f1ed5ef5bf2779",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/907 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa14fad7c88d4b6b9d7c57c9ced0a0b0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/2.97G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at nielsr/udop-large were not used when initializing UdopEncoderModel: ['decoder.block.5.layer.0.SelfAttention.v.weight', 'decoder.block.13.layer.2.DenseReluDense.wo.weight', 'decoder.block.16.layer.1.EncDecAttention.q.weight', 'decoder.block.1.layer.2.DenseReluDense.wi.weight', 'decoder.block.2.layer.2.layer_norm.weight', 'decoder.block.2.layer.1.EncDecAttention.q.weight', 'decoder.block.7.layer.1.EncDecAttention.k.weight', 'decoder.block.22.layer.0.SelfAttention.v.weight', 'decoder.block.2.layer.2.DenseReluDense.wo.weight', 'decoder.block.2.layer.0.SelfAttention.o.weight', 'decoder.block.9.layer.0.layer_norm.weight', 'decoder.block.19.layer.2.DenseReluDense.wi.weight', 'decoder.block.5.layer.0.layer_norm.weight', 'decoder.block.4.layer.1.EncDecAttention.v.weight', 'decoder.block.4.layer.0.SelfAttention.k.weight', 'decoder.block.4.layer.1.layer_norm.weight', 'decoder.block.8.layer.1.layer_norm.weight', 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'decoder.block.4.layer.0.SelfAttention.q.weight', 'decoder.block.1.layer.0.SelfAttention.q.weight', 'decoder.block.17.layer.0.SelfAttention.v.weight', 'decoder.block.17.layer.2.layer_norm.weight', 'decoder.block.0.layer.1.EncDecAttention.q.weight', 'decoder.block.16.layer.2.layer_norm.weight', 'decoder.block.20.layer.0.SelfAttention.v.weight', 'decoder.block.3.layer.2.DenseReluDense.wo.weight', 'decoder.block.3.layer.1.EncDecAttention.q.weight', 'decoder.block.23.layer.0.SelfAttention.o.weight']\n",
            "- This IS expected if you are initializing UdopEncoderModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing UdopEncoderModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "from transformers import UdopEncoderModel, UdopPreTrainedModel, UdopConfig\n",
        "from transformers.modeling_outputs import TokenClassifierOutput\n",
        "from torch import nn\n",
        "\n",
        "class UdopForTokenClassification(UdopPreTrainedModel):\n",
        "  def __init__(self, config):\n",
        "    super().__init__(config)\n",
        "\n",
        "    self.udop = UdopEncoderModel.from_pretrained(\"microsoft/udop-large\")\n",
        "    self.num_labels = config.num_labels\n",
        "\n",
        "    self.dropout = nn.Dropout(0.5)\n",
        "    self.classifier = nn.Linear(config.hidden_size, config.num_labels)\n",
        "\n",
        "  def forward(self, input_ids, bbox, attention_mask, pixel_values, labels):\n",
        "    outputs = self.udop(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, pixel_values=pixel_values)\n",
        "    sequence_output = outputs[0]\n",
        "\n",
        "    seq_length = input_ids.shape[1]\n",
        "    # only take the text part of the output representations\n",
        "    sequence_output = outputs[0][:, :seq_length]\n",
        "    sequence_output = self.dropout(sequence_output)\n",
        "\n",
        "    logits = self.classifier(sequence_output)\n",
        "\n",
        "    loss = None\n",
        "    if labels is not None:\n",
        "        loss_fct = nn.CrossEntropyLoss()\n",
        "        loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "\n",
        "    return TokenClassifierOutput(\n",
        "        loss=loss,\n",
        "        logits=logits,\n",
        "        hidden_states=outputs.hidden_states,\n",
        "        attentions=outputs.attentions,\n",
        "    )\n",
        "\n",
        "config = UdopConfig(id2label=id2label, label2id=label2id)\n",
        "model = UdopForTokenClassification(config)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KjGxK1WnZ6In"
      },
      "source": [
        "## Define TrainingArguments + Trainer\n",
        "\n",
        "Next we define the `TrainingArguments`, which define all hyperparameters related to training. Note that there is a huge amount of parameters to tweak, check the [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) for more info."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AFdXJ3ZDU6yg"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "training_args = TrainingArguments(output_dir=\"test\",\n",
        "                                  max_steps=3000,\n",
        "                                  warmup_ratio=0.1,\n",
        "                                  per_device_train_batch_size=1,\n",
        "                                  per_device_eval_batch_size=1,\n",
        "                                  gradient_accumulation_steps=8,\n",
        "                                  eval_accumulation_steps=8,\n",
        "                                  learning_rate=5e-5,\n",
        "                                  evaluation_strategy=\"steps\",\n",
        "                                  eval_steps=100,\n",
        "                                  load_best_model_at_end=True,\n",
        "                                  metric_for_best_model=\"f1\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4eUBPS4DT6jO"
      },
      "source": [
        "We can now instantiate a Trainer, with the model and args defined above. We also provide our datasets, as well as a \"default data collator\" - which will batch the examples using `torch.stack`. We also provide our `compute_metrics` function defined above."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B92MlZapU4Qc"
      },
      "outputs": [],
      "source": [
        "from transformers.data.data_collator import default_data_collator\n",
        "\n",
        "# Initialize our Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    tokenizer=processor,\n",
        "    data_collator=default_data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTLQnEtY4rx6"
      },
      "source": [
        "## Train the model\n",
        "\n",
        "Let's train!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "cqcq7rzlVDOE",
        "outputId": "bd8a1c02-6bf1-4254-a6b3-32de9ef0a35e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:407: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:866: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='990' max='3000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 990/3000 1:17:30 < 2:37:41, 0.21 it/s, Epoch 53.10/167]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.577256</td>\n",
              "      <td>0.370475</td>\n",
              "      <td>0.402246</td>\n",
              "      <td>0.385707</td>\n",
              "      <td>0.488029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.728565</td>\n",
              "      <td>0.734486</td>\n",
              "      <td>0.845840</td>\n",
              "      <td>0.786240</td>\n",
              "      <td>0.764395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.696579</td>\n",
              "      <td>0.813422</td>\n",
              "      <td>0.872384</td>\n",
              "      <td>0.841872</td>\n",
              "      <td>0.796440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.834695</td>\n",
              "      <td>0.828458</td>\n",
              "      <td>0.852986</td>\n",
              "      <td>0.840543</td>\n",
              "      <td>0.779742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.775400</td>\n",
              "      <td>0.912845</td>\n",
              "      <td>0.846730</td>\n",
              "      <td>0.865748</td>\n",
              "      <td>0.856133</td>\n",
              "      <td>0.794966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.775400</td>\n",
              "      <td>0.948970</td>\n",
              "      <td>0.856354</td>\n",
              "      <td>0.870342</td>\n",
              "      <td>0.863291</td>\n",
              "      <td>0.796808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.775400</td>\n",
              "      <td>1.021489</td>\n",
              "      <td>0.837970</td>\n",
              "      <td>0.876468</td>\n",
              "      <td>0.856786</td>\n",
              "      <td>0.798772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.775400</td>\n",
              "      <td>1.033358</td>\n",
              "      <td>0.854094</td>\n",
              "      <td>0.878509</td>\n",
              "      <td>0.866130</td>\n",
              "      <td>0.805034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.775400</td>\n",
              "      <td>1.123953</td>\n",
              "      <td>0.853659</td>\n",
              "      <td>0.857580</td>\n",
              "      <td>0.855615</td>\n",
              "      <td>0.799509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:866: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PMyH4vADWYaq"
      },
      "source": [
        "## Evaluate the model\n",
        "\n",
        "NOTE: we end up with an F1 score of about 90%. Here's what I got on a typical run:\n",
        "\n",
        "```\n",
        "Step\tTraining Loss\tValidation Loss\tPrecision\tRecall\tF1\tAccuracy\n",
        "100\tNo log\t0.725903\t0.812686\t0.837162\t0.824742\t0.781338\n",
        "200\tNo log\t0.807471\t0.814045\t0.840225\t0.826928\t0.782320\n",
        "300\tNo log\t0.856688\t0.812919\t0.867279\t0.839220\t0.799632\n",
        "400\tNo log\t0.995948\t0.839463\t0.862175\t0.850667\t0.795089\n",
        "500\t0.178400\t0.994649\t0.825769\t0.863706\t0.844311\t0.798649\n",
        "600\t0.178400\t1.012888\t0.844751\t0.874936\t0.859579\t0.808349\n",
        "700\t0.178400\t1.057748\t0.835127\t0.871363\t0.852860\t0.806262\n",
        "800\t0.178400\t1.069149\t0.840730\t0.870342\t0.855280\t0.803560\n",
        "900\t0.178400\t1.103710\t0.845274\t0.867279\t0.856135\t0.802455\n",
        "1000\t0.039800\t1.107941\t0.844246\t0.868811\t0.856352\t0.802947\n",
        "```\n",
        "Compared to LayoutLMv3:\n",
        "```\n",
        "Step\tTraining Loss\tVal Loss\tPrecision\tRecall\tF1\tAccuracy\n",
        "100\t  No log\t  0.716025\t0.752040\t0.824143\t0.786442\t0.780459\n",
        "200\t  No log\t  0.584986\t0.828558\t0.876304\t0.851762\t0.801973\n",
        "300\t  No log\t  0.525926\t0.859583\t0.900149\t0.879398\t0.833947\n",
        "400\t  No log\t  0.492821\t0.881413\t0.904620\t0.892866\t0.854630\n",
        "500\t  0.561200\t0.528126\t0.858382\t0.885246\t0.871607\t0.852490\n",
        "600\t  0.561200\t0.547107\t0.888023\t0.906110\t0.896976\t0.847973\n",
        "700\t  0.561200\t0.555438\t0.887338\t0.915549\t0.901222\t0.859384\n",
        "800\t  0.561200\t0.582942\t0.881471\t0.905117\t0.893137\t0.854749\n",
        "900\t  0.561200\t0.599762\t0.891051\t0.910084\t0.900467\t0.852015\n",
        "1000\t0.133400\t0.608207\t0.887222\t0.910581\t0.898750\t0.847855\n",
        "````\n",
        "\n",
        "However, this score cannot be directly compared to LayoutLM and LayoutLMv2, as LayoutLMv3 employs so-called **segment position embeddings** (inspired by [StructuralLM](https://arxiv.org/abs/2105.11210)). This means that several tokens that belong to the same \"segment\" (let's say, an address) get the same bounding box coordinates, and in return the same 2D position embeddings.\n",
        "\n",
        "This is also mentioned in the paper:\n",
        ">  Note that LayoutLMv3 and StructuralLM use segment-level layout positions, while the other works use word-level layout positions. The use of segment-level positions may benefit the semantic entity labeling task on FUNSD [25], so the two types of work are not directly comparable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w86kFoyXX8QC"
      },
      "outputs": [],
      "source": [
        "trainer.evaluate()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wDWVaw74YXgW"
      },
      "source": [
        "## Inference\n",
        "\n",
        "You can load the model for inference as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F4gNkWLjW42_"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForTokenClassification\n",
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"/content/test/checkpoint-1000\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVNDti-FaGM9"
      },
      "source": [
        "Let's take an example of the training dataset to show inference."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Na_iKGGmYdiW"
      },
      "outputs": [],
      "source": [
        "example = dataset[\"test\"][0]\n",
        "print(example.keys())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y5N3VdXtaI7T"
      },
      "source": [
        "We first prepare it for the model using the processor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q6CfsESpYqJF"
      },
      "outputs": [],
      "source": [
        "image = example[\"image\"]\n",
        "words = example[\"tokens\"]\n",
        "boxes = example[\"bboxes\"]\n",
        "word_labels = example[\"ner_tags\"]\n",
        "\n",
        "encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors=\"pt\")\n",
        "for k,v in encoding.items():\n",
        "  print(k,v.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8fPoV-L3aK5I"
      },
      "source": [
        "Next, we do a forward pass. We use torch.no_grad() as we don't require gradient computation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V6GvYURlY5ZV"
      },
      "outputs": [],
      "source": [
        "with torch.no_grad():\n",
        "  outputs = model(**encoding)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-4euZINaR2j"
      },
      "source": [
        "The model outputs logits of shape (batch_size, seq_len, num_labels)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c3IKHCKNY8yi"
      },
      "outputs": [],
      "source": [
        "logits = outputs.logits\n",
        "logits.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fsWG2v4RaVro"
      },
      "source": [
        "We take the highest score for each token, using argmax. This serves as the predicted label for each token."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ItY1FCYlY-k9"
      },
      "outputs": [],
      "source": [
        "predictions = logits.argmax(-1).squeeze().tolist()\n",
        "print(predictions)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IlOnJGAEadu2"
      },
      "source": [
        "Let's compare this to the ground truth: note that many labels are -100, as we're only labeling the first subword token of each word.\n",
        "\n",
        "NOTE: at \"true inference\" time, you don't have access to labels, see the latest section of this notebook how you can use `offset_mapping` in that case."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xty8NxghZK5k"
      },
      "outputs": [],
      "source": [
        "labels = encoding.labels.squeeze().tolist()\n",
        "print(labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "woF8VhPRakhG"
      },
      "source": [
        "So let's only compare predictions and labels at positions where the label isn't -100. We also want to have the bounding boxes of these (unnormalized):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2AMcE0SqZWnr"
      },
      "outputs": [],
      "source": [
        "def unnormalize_box(bbox, width, height):\n",
        "     return [\n",
        "         width * (bbox[0] / 1000),\n",
        "         height * (bbox[1] / 1000),\n",
        "         width * (bbox[2] / 1000),\n",
        "         height * (bbox[3] / 1000),\n",
        "     ]\n",
        "\n",
        "token_boxes = encoding.bbox.squeeze().tolist()\n",
        "width, height = image.size\n",
        "\n",
        "true_predictions = [model.config.id2label[pred] for pred, label in zip(predictions, labels) if label != - 100]\n",
        "true_labels = [model.config.id2label[label] for prediction, label in zip(predictions, labels) if label != -100]\n",
        "true_boxes = [unnormalize_box(box, width, height) for box, label in zip(token_boxes, labels) if label != -100]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZInOqXDIcvjR"
      },
      "outputs": [],
      "source": [
        "from PIL import ImageDraw, ImageFont\n",
        "\n",
        "draw = ImageDraw.Draw(image)\n",
        "\n",
        "font = ImageFont.load_default()\n",
        "\n",
        "def iob_to_label(label):\n",
        "    label = label[2:]\n",
        "    if not label:\n",
        "      return 'other'\n",
        "    return label\n",
        "\n",
        "label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'}\n",
        "\n",
        "for prediction, box in zip(true_predictions, true_boxes):\n",
        "    predicted_label = iob_to_label(prediction).lower()\n",
        "    draw.rectangle(box, outline=label2color[predicted_label])\n",
        "    draw.text((box[0] + 10, box[1] - 10), text=predicted_label, fill=label2color[predicted_label], font=font)\n",
        "\n",
        "image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tuDgga5deJgs"
      },
      "source": [
        "Compare this to the ground truth:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8YvlrMZ_djbF"
      },
      "outputs": [],
      "source": [
        "image = example[\"image\"]\n",
        "image = image.convert(\"RGB\")\n",
        "\n",
        "draw = ImageDraw.Draw(image)\n",
        "\n",
        "for word, box, label in zip(example['tokens'], example['bboxes'], example['ner_tags']):\n",
        "  actual_label = iob_to_label(id2label[label]).lower()\n",
        "  box = unnormalize_box(box, width, height)\n",
        "  draw.rectangle(box, outline=label2color[actual_label], width=2)\n",
        "  draw.text((box[0] + 10, box[1] - 10), actual_label, fill=label2color[actual_label], font=font)\n",
        "\n",
        "image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fHrlwJTO0Te"
      },
      "source": [
        "## Note: inference when you don't have labels\n",
        "\n",
        "The code above used the `labels` to determine which tokens were at the start of a particular word or not. Of course, at inference time, you don't have access to any labels. In that case, you can leverage the `offset_mapping` returned by the tokenizer. I do have a notebook for that (for LayoutLMv2, but it's equivalent for LayoutLMv3) [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/FUNSD/True_inference_with_LayoutLMv2ForTokenClassification_%2B_Gradio_demo.ipynb)."
      ]
    },
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